






'''simple docstring'''





import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


_UpperCamelCase						:						str  =					['bart.large', 'bart.large.mnli', 'bart.large.cnn', 'bart_xsum/model.pt']
_UpperCamelCase						:						str  =					{'bart.large': BartModel, 'bart.large.mnli': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('0.9.0'):
						raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
_UpperCamelCase						:						List[str]  =					logging.get_logger(__name__)

_UpperCamelCase						:						str  =					' Hello world! cécé herlolip'

_UpperCamelCase						:						Union[str, Any]  =					[
    ('model.classification_heads.mnli.dense.weight', 'classification_head.dense.weight'),
    ('model.classification_heads.mnli.dense.bias', 'classification_head.dense.bias'),
    ('model.classification_heads.mnli.out_proj.weight', 'classification_head.out_proj.weight'),
    ('model.classification_heads.mnli.out_proj.bias', 'classification_head.out_proj.bias'),
]
def __UpperCAmelCase   (					A					:			Tuple		)    ->   Tuple:
			UpperCAmelCase_      :       List[str]          =       [
			    '''encoder.version''',
			    '''decoder.version''',
			    '''model.encoder.version''',
			    '''model.decoder.version''',
			    '''_float_tensor''',
			]
			for k in ignore_keys:
						state_dict.pop(A			,       A		)
def __UpperCAmelCase   (					A					:			Union[str, Any]			,       A					:			str			,       A					:			List[Any]		)    ->   Optional[Any]:
			UpperCAmelCase_      :       str          =       dct.pop(A		)
			UpperCAmelCase_      :       Dict          =       val
def __UpperCAmelCase   (					A					:			Union[str, Any]		)    ->   Optional[int]:
			UpperCAmelCase_      :       List[str]          =       torch.load(A			,       map_location='''cpu'''		)
			UpperCAmelCase_      :       str          =       torch.hub.load('''pytorch/fairseq'''			,       '''bart.large.cnn'''		).eval()
			hub_interface.model.load_state_dict(sd['''model''']		)
			return hub_interface
def __UpperCAmelCase   (					A					:			Optional[int]		)    ->   Optional[int]:
			UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       emb.weight.shape
			UpperCAmelCase_      :       Optional[Any]          =       nn.Linear(A			,       A			,       bias=A		)
			UpperCAmelCase_      :       Dict          =       emb.weight.data
			return lin_layer
@torch.no_grad()
def __UpperCAmelCase   (					A					:			Optional[Any]			,       A					:			Optional[Any]			,       A					:			Optional[int]=None		)    ->   int:
			if not os.path.exists(A		):
						UpperCAmelCase_      :       int          =       torch.hub.load('''pytorch/fairseq'''			,       A		).eval()
			else:
						UpperCAmelCase_      :       List[str]          =       load_xsum_checkpoint(A		)

			bart.model.upgrade_state_dict(bart.model.state_dict()		)
			if hf_checkpoint_name is None:
						UpperCAmelCase_      :       Dict          =       checkpoint_path.replace('''.'''			,       '''-'''		)
			UpperCAmelCase_      :       List[Any]          =       BartConfig.from_pretrained(A		)
			UpperCAmelCase_      :       Optional[Any]          =       bart.encode(A		).unsqueeze(0		)
			UpperCAmelCase_      :       int          =       BartTokenizer.from_pretrained(A		).encode(A			,       return_tensors='''pt'''		).unsqueeze(0		)
			if not torch.eq(A			,       A		).all():
						raise ValueError(
						    F"converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"		)

			if checkpoint_path == "bart.large.mnli":
						UpperCAmelCase_      :       int          =       bart.state_dict()
						remove_ignore_keys_(A		)
						UpperCAmelCase_      :       Optional[int]          =       state_dict['''model.decoder.embed_tokens.weight''']
						for src, dest in mnli_rename_keys:
									rename_key(A			,       A			,       A		)
						UpperCAmelCase_      :       Union[str, Any]          =       BartForSequenceClassification(A		).eval()
						model.load_state_dict(A		)
						UpperCAmelCase_      :       str          =       bart.predict('''mnli'''			,       A			,       return_logits=A		)
						UpperCAmelCase_      :       List[str]          =       model(A		)[0]  # logits
			else:  # no classification heads to worry about
						UpperCAmelCase_      :       Union[str, Any]          =       bart.model.state_dict()
						remove_ignore_keys_(A		)
						UpperCAmelCase_      :       Union[str, Any]          =       state_dict['''decoder.embed_tokens.weight''']
						UpperCAmelCase_      :       int          =       bart.extract_features(A		)
						if hf_checkpoint_name == "facebook/bart-large":
									UpperCAmelCase_      :       List[Any]          =       BartModel(A		).eval()
									model.load_state_dict(A		)
									UpperCAmelCase_      :       int          =       model(A		).model[0]
						else:
									UpperCAmelCase_      :       str          =       BartForConditionalGeneration(A		).eval()  # an existing summarization ckpt
									model.model.load_state_dict(A		)
									if hasattr(A			,       '''lm_head'''		):
												UpperCAmelCase_      :       Optional[Any]          =       make_linear_from_emb(model.model.shared		)
									UpperCAmelCase_      :       int          =       model.model(A		)[0]

    # Check results
			if fairseq_output.shape != new_model_outputs.shape:
						raise ValueError(
						    F"`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"		)
			if (fairseq_output != new_model_outputs).any().item():
						raise ValueError('''Some values in `fairseq_output` are different from `new_model_outputs`'''		)
			Path(A		).mkdir(exist_ok=A		)
			model.save_pretrained(A		)


if __name__ == "__main__":
						_UpperCamelCase						:						Any  =					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
						)
						parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument(
						    '--hf_config', default=None, type=str, help='Which huggingface architecture to use: bart-large-xsum'
						)
						_UpperCamelCase						:						Union[str, Any]  =					parser.parse_args()
						convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)










'''simple docstring'''





import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_UpperCamelCase						:						str  =					{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class  snake_case__      (					nn.Module):




					def __init__(  self  :		Optional[int]				, _A  :		Tuple      )    ->							List[str]:
								super().__init__()
								UpperCAmelCase_      :       Tuple          =       torchvision.models.resnetaaa(pretrained=_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       list(model.children()      )[:-2]
								UpperCAmelCase_      :       Union[str, Any]          =       nn.Sequential(*_A      )
								UpperCAmelCase_      :       List[Any]          =       nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]      )





					def 		A      (  self  :		str				, _A  :		Optional[int]      )    ->							str:
								# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
								UpperCAmelCase_      :       List[str]          =       self.pool(self.model(_A      )      )
								UpperCAmelCase_      :       Tuple          =       torch.flatten(_A				, start_dim=2      )
								UpperCAmelCase_      :       Union[str, Any]          =       out.transpose(1				, 2      ).contiguous()
								return out  # BxNx2048






class  snake_case__      (					UpperCamelCase):




					def __init__(  self  :		Optional[int]				, _A  :		int				, _A  :		str				, _A  :		int				, _A  :		Dict				, _A  :		int      )    ->							List[str]:
								UpperCAmelCase_      :       Any          =       [json.loads(_A      ) for l in open(_A      )]
								UpperCAmelCase_      :       Tuple          =       os.path.dirname(_A      )
								UpperCAmelCase_      :       Any          =       tokenizer
								UpperCAmelCase_      :       Optional[Any]          =       labels
								UpperCAmelCase_      :       List[str]          =       len(_A      )
								UpperCAmelCase_      :       int          =       max_seq_length

								UpperCAmelCase_      :       str          =       transforms




					def __len__(  self  :		Tuple      )    ->							Tuple:
								return len(self.data      )




					def __getitem__(  self  :		Union[str, Any]				, _A  :		Tuple      )    ->							List[Any]:
								UpperCAmelCase_      :       Tuple          =       torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']				, add_special_tokens=_A      )      )
								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       sentence[0], sentence[1:-1], sentence[-1]
								UpperCAmelCase_      :       List[str]          =       sentence[: self.max_seq_length]

								UpperCAmelCase_      :       List[str]          =       torch.zeros(self.n_classes      )
								UpperCAmelCase_      :       Optional[int]          =       1

								UpperCAmelCase_      :       Tuple          =       Image.open(os.path.join(self.data_dir				, self.data[index]['''img''']      )      ).convert('''RGB'''      )
								UpperCAmelCase_      :       List[Any]          =       self.transforms(_A      )

								return {
								    "image_start_token": start_token,
								    "image_end_token": end_token,
								    "sentence": sentence,
								    "image": image,
								    "label": label,
								}





					def 		A      (  self  :		Tuple      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Tuple          =       Counter()
								for row in self.data:
											label_freqs.update(row['''label''']      )
								return label_freqs






def __UpperCAmelCase   (					A					:			Tuple		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       int          =       [len(row['''sentence''']		) for row in batch]
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       len(A		), max(A		)

			UpperCAmelCase_      :       Tuple          =       torch.zeros(A			,       A			,       dtype=torch.long		)
			UpperCAmelCase_      :       Dict          =       torch.zeros(A			,       A			,       dtype=torch.long		)

			for i_batch, (input_row, length) in enumerate(zip(A			,       A		)		):
						UpperCAmelCase_      :       int          =       input_row['''sentence''']
						UpperCAmelCase_      :       Optional[Any]          =       1

			UpperCAmelCase_      :       Union[str, Any]          =       torch.stack([row['''image'''] for row in batch]		)
			UpperCAmelCase_      :       Optional[int]          =       torch.stack([row['''label'''] for row in batch]		)
			UpperCAmelCase_      :       Any          =       torch.stack([row['''image_start_token'''] for row in batch]		)
			UpperCAmelCase_      :       int          =       torch.stack([row['''image_end_token'''] for row in batch]		)

			return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			return [
			    "Crime",
			    "Drama",
			    "Thriller",
			    "Action",
			    "Comedy",
			    "Romance",
			    "Documentary",
			    "Short",
			    "Mystery",
			    "History",
			    "Family",
			    "Adventure",
			    "Fantasy",
			    "Sci-Fi",
			    "Western",
			    "Horror",
			    "Sport",
			    "War",
			    "Music",
			    "Musical",
			    "Animation",
			    "Biography",
			    "Film-Noir",
			]







def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			return transforms.Compose(
			    [
			        transforms.Resize(2_5_6		),
			        transforms.CenterCrop(2_2_4		),
			        transforms.ToTensor(),
			        transforms.Normalize(
			            mean=[0.46777044, 0.44531429, 0.40661017]			,       std=[0.12221994, 0.12145835, 0.14380469]			,       ),
			    ]		)


